A Survey on Visual Traffic Simulation: Models, Evaluations, and Applications in Autonomous Driving

Authors

Qianwen Chao, Xidian University
Huikun Bi, Institute of Computing Technology Chinese Academy of Sciences
Weizi Li, The University of North Carolina at Chapel Hill
Tianlu Mao, Institute of Computing Technology Chinese Academy of Sciences
Zhaoqi Wang, Institute of Computing Technology Chinese Academy of Sciences
Ming C. Lin, University of Maryland, College Park

Abstract

Virtualized traffic via various simulation models and real-world traffic data are promising approaches to reconstruct detailed traffic flows. A variety of applications can benefit from the virtual traffic, including, but not limited to, video games, virtual reality, traffic engineering and autonomous driving. In this survey, we provide a comprehensive review on the state-of-the-art techniques for traffic simulation and animation. We start with a discussion on three classes of traffic simulation models applied at different levels of detail. Then, we introduce various data-driven animation techniques, including existing data collection methods, and the validation and evaluation of simulated traffic flows. Next, we discuss how traffic simulations can benefit the training and testing of autonomous vehicles. Finally, we discuss the current states of traffic simulation and animation and suggest future research directions.

Publication Title

Computer Graphics Forum

Recommended Citation

Chao, Q., Bi, H., Li, W., Mao, T., Wang, Z., & Lin, M. (2019). A Survey on Visual Traffic Simulation: Models, Evaluations, and Applications in Autonomous Driving. Computer Graphics Forum https://doi.org/10.1111/cgf.13803